---
skill_id: aie-quality-cost-optimizer
evaluation: free
price_usd_full: 0.07
challenge: https://lvlltd.com/api/pay?skill=aie-quality-cost-optimizer
outline: https://lvlltd.com/skills/aie-quality-cost-optimizer/outline.json
standards: x402, ERC-7857
---

# Free sample — Quality-vs-Cost Optimizer Agent

**Skill ID:** `aie-quality-cost-optimizer`  
**Full unlock:** $0.07 USDC via x402 on Base  
**Challenge:** https://lvlltd.com/api/pay?skill=aie-quality-cost-optimizer  
**Outline JSON:** https://lvlltd.com/skills/aie-quality-cost-optimizer/outline.json

## Summary

Pick model/path on quality–cost Pareto for inference jobs.

## When to use

- Any agent goal that matches the summary/capabilities below

## Decision criteria (buy vs skip)

- You get capability: quality proxy metrics for the task class
- Budget fits: full unlock is $0.07 USDC on Base
- You can automate unlock via HTTP 402 without a browser
- You only need teaser/outline first — sealed src/ops stay locked until payment

## Outline (preview)

1. Define quality proxy metrics for the task class
2. Build Pareto frontier of model/path vs cost
3. Pick path under quality floor and budget ceiling
4. Log chosen path for post-hoc audit
5. Emit optimizer-decision.json + frontier table

## Capabilities (preview)

- quality proxy metrics for the task class
- Pareto frontier of model/path vs cost
- Pick path under quality floor and budget ceiling
- chosen path for post-hoc audit

## What you get after payment

- SKILL.md (teaser public)
- agent-install.json (public meta)
- src/index.js (stub public; full after unlock)
- Full sealed: src/, schemas/, ops/* (when present) after verified X-PAYMENT

## Agent buy path (zero human UI)

```
GET  https://lvlltd.com/skills/aie-quality-cost-optimizer/outline.json
GET  https://lvlltd.com/api/pay?skill=aie-quality-cost-optimizer   → HTTP 402
# transfer maxAmountRequired USDC to payTo on Base
POST https://lvlltd.com/api/pay
  X-PAYMENT: {"txHash":"0x…","skill":"aie-quality-cost-optimizer"}
# response.sealed_pack.files = full pack
```

## Sample excerpt

# Quality-vs-Cost Optimizer Agent

Pick model/path on quality–cost Pareto for inference jobs.

This pack replaces generic boilerplate with a concrete method for **Quality-vs-Cost Optimizer Agent**. Free evaluation steps:

1. Define quality proxy metrics for the task class
2. Build Pareto frontier of model/path vs cost
3. Pick path under quality floor and budget ceiling
4. Log chosen path for post-hoc audit
5. Emit optimizer-decision.json + frontier table

When to use: Pick model/path on quality–cost Pareto for inference jobs..

**Price:** $0.07 / call USDC on Base via x402 unlock.

**Standards:** x402 payments · ERC-7857 sealed skill model · agent-swarm ready.

## Outline

1. Define quality proxy metrics for the task class
2. Build Pareto frontier of model/path vs cost
3. Pick path under quality floor and budget ceiling
4. Log chosen path for post-hoc audit
5. Emit optimizer-decision.json + frontier table